2026/01/01 by Aheli Saha, René Schuster, Didier Stricker
Computer Science · Engineering · #Distributed Sensor Networks and Detection Algorithms #Target Tracking and Data Fusion in Sensor Networks #Advanced Memory and Neural Computing
paper · pdf · doi:10.5220/0014450400004067
Bio-inspired event cameras have recently attracted significant research due to their asynchronous and low-latency capabilities. These features provide a high dynamic range and significantly reduce motion blur. However, because of the novelty in the nature of their output signals, there is a gap in the variability of available data and a lack of extensive analysis of the parameters characterizing their signals. This paper addresses these issues by providing readers with an in-depth understanding of how intrinsic parameters affect the performance of a model trained on event data, specifically for object detection. We also use our findings to expand the capabilities of the downstream model towards sensor-agnostic robustness.